The optimization problem in (REF ) is non-convex and difficult to address.
Indeed, it involve objective functions and constraints that apply on both the covariance matrix and its inverse.
An approach to handle such multiple intricate constraints is to resort to variable splitting and alternating direction method of multipliers [1]} which has, for example, been considered for Laplacian-constrained models in [2]}, [3]}.
In this work, we propose a new and more direct approach by harnessing Riemannian optimization [4]}, [5]}.
